code: def __init__(
      self,
      obs_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      num_outputs: Optional[int],
      model_config: ModelConfigDict,
      name: str,
      policy_model_config: ModelConfigDict = None,
      q_model_config: ModelConfigDict = None,
      twin_q: bool = False,
      initial_alpha: float = 1.0,
      target_entropy: Optional[float] = None,
  ):
      super().__init__(
          obs_space=obs_space,
          action_space=action_space,
          num_outputs=num_outputs,
          model_config=model_config,
          name=name,
          policy_model_config=policy_model_config,
          q_model_config=q_model_config,
          twin_q=twin_q,
          initial_alpha=initial_alpha,
          target_entropy=target_entropy,
      )
      self.use_prev_action = (
          model_config["lstm_use_prev_action"]
          or policy_model_config["lstm_use_prev_action"]
          or q_model_config["lstm_use_prev_action"]
      )
  
      self.use_prev_reward = (
          model_config["lstm_use_prev_reward"]
          or policy_model_config["lstm_use_prev_reward"]
          or q_model_config["lstm_use_prev_reward"]
      )
      if self.use_prev_action:
          self.view_requirements[
              SampleBatch.PREV_ACTIONS
          ] = ViewRequirement(
              SampleBatch.ACTIONS, space=self.action_space, shift=-1
          )
      if self.use_prev_reward:
          self.view_requirements[
              SampleBatch.PREV_REWARDS
          ] = ViewRequirement(SampleBatch.REWARDS, shift=-1)
  
file_path: ray/rllib/algorithms/sac/rnnsac_torch_model.py
imports: import gymnasium
  from typing import Optional, List, Dict
  from ray.rllib.algorithms.sac.sac_torch_model import SACTorchModel
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils import override, force_list
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
label_desc: Use image node_id 1 to create a new RNNSACTorchModel object from inherited base classes: SACTorchModel with example: obj = RNNSACTorchModel(obs_space, action_space, num_outputs, model_config, name, policy_model_config, q_model_config, twin_q, initial_alpha, target_entropy)